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Montopoli, M., G. Vulpiani, D. Cimini, E. Picciotti, and F. S. Marzano. "Interpretation of observed microwave signatures from ground dual polarization radar and space multi-frequency radiometer for the 2011 Grímsvötn volcanic eruption." Atmospheric Measurement Techniques 7, no. 2 (2014): 537–52. http://dx.doi.org/10.5194/amt-7-537-2014.
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Abstract. The important role played by ground-based microwave weather radars for the monitoring of volcanic ash clouds has been recently demonstrated. The potential of microwaves from satellite passive and ground-based active sensors to estimate near-source volcanic ash cloud parameters has been also proposed, though with little investigation of their synergy and the role of the radar polarimetry. The goal of this work is to show the potentiality and drawbacks of the X-band dual polarization (DPX) radar measurements through the data acquired during the latest Grímsvötn volcanic eruptions that took place in May 2011 in Iceland. The analysis is enriched by the comparison between DPX data and the observations from the satellite Special Sensor Microwave Imager/Sounder (SSMIS) and a C-band single polarization (SPC) radar. SPC, DPX, and SSMIS instruments cover a large range of the microwave spectrum, operating respectively at 5.4, 3.2, and 0.16–1.6 cm wavelengths.
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Guyot, Adrien, Jordan P. Brook, Alain Protat, et al. "Segmentation of polarimetric radar imagery using statistical texture." Atmospheric Measurement Techniques 16, no. 19 (2023): 4571–88. http://dx.doi.org/10.5194/amt-16-4571-2023.
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Abstract. Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification.
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Gogineni, S., J. B. Yan, J. Paden, et al. "Bed topography of Jakobshavn Isbræ, Greenland, and Byrd Glacier, Antarctica." Journal of Glaciology 60, no. 223 (2014): 813–33. http://dx.doi.org/10.3189/2014jog14j129.
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AbstractThis paper presents the bed topography of Jakobshavn Isbræ, Greenland, and Byrd Glacier, Antarctica, derived from sounding these glaciers with high-sensitivity radars. To understand the processes causing the speed-up and retreat of outlet glaciers, and to enable the development of next-generation ice-sheet models, we need information on bed topography and basal conditions. To this end, we performed measurements with the progressively improved Multichannel Coherent Radar Depth Sounder/Imager (MCoRDS/I). We processed the data from each antenna-array element using synthetic aperture radar algorithms to improve radar sensitivity and reduce along-track surface clutter. We then applied array and image-processing algorithms to extract the weak bed echoes buried in off-vertical scatter (cross-track surface clutter). At Jakobshavn Isbræ, we observed 2.7 km thick ice ~30 km upstream of the calving front and ~850 m thick ice at the calving front. We also observed echoes from multiple interfaces near the bed. We applied the MUSIC algorithm to the data to derive the direction of arrival of the signals. This analysis revealed that clutter is dominated by the ice surface at Jakobshavn Isbræ. At Byrd Glacier, we found ~3.62 km thick ice, as well as a subglacial trench ~3.05 km below sea level. We used ice thickness information derived from radar data in conjunction with surface elevation data to generate bed maps for these two critical glaciers. The performance of current radars must be improved further by ~15 dB to fully sound the deepest part of Byrd Glacier. Unmanned aerial systems equipped with radars that can be flown over lines spaced as close as 5 m apart in the cross-track direction to synthesize a two-dimensional aperture would be ideal for collecting fine-resolution data over glaciers like Jakobshavn near their grounding lines.
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Frame, D. J., B. N. Lawrence, G. J. Fraser, and M. D. Burrage. "A comparison between mesospheric wind measurements made near Christchurch (44°S, 173°E) using the high resolution doppler imager (HRDI) and a medium frequency (MF) radar." Annales Geophysicae 18, no. 5 (2000): 555–65. http://dx.doi.org/10.1007/s00585-000-0555-3.
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Abstract. We report on the comparison of winds measured by a medium frequency (MF) radar near Christchurch, New Zealand, and by the high resolution doppler imager (HRDI). Previous comparisons have demonstrated that there can be significant differences in the winds obtained by the two techniques, and our results are no different. However, these data show relatively good agreement in the meridional direction, but large differences in the zonal direction, where the radar is regularly measuring the zonal wind as too easterly. To do the comparison, overpasses from the satellite must be obtained when it is close to the radar site. The radar data are averaged in time around the overpass because we know the radars sample phenomena which have spatial and temporal scales which make them invisible to HRDI. There are a limited number of overpass comparisons which limit our confidence in these results, but a detailed analysis of these data show that the proximity of the overpass is often an important factor in the differences obtained. Other factors examined include the influence of the local time of the overpass, and the amount of radar data averaged around the overpass time.Key words: Atmospheric composition and structure (instruments and techniques) · Meteorology and atmospheric dynamics (middle atmosphere dynamics; instruments and techniques)
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Hasebe, F., T. Tsuda, T. Nakamura, and M. D. Burrage. "Validation of HRDI MLT winds with meteor radars." Annales Geophysicae 15, no. 9 (1997): 1142–57. http://dx.doi.org/10.1007/s00585-997-1142-7.
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Abstract. A validation study of the mesospheric and lower-thermospheric (MLT) wind velocities measured by the High-Resolution Doppler Imager (HRDI) on board the Upper-Atmosphere Research Satellite (UARS) has been carried out, comparing with observations by meteor radars located at Shigaraki, Japan and Jakarta, Indonesia. The accuracy of the HRDI winds relative to the meteor radars is obtained by a series of simultaneous wind measurements at the time of UARS overpasses. Statistical tests on the difference in the wind vectors observed by HRDI and the meteor radars are applied to determine whether the wind speed has been overestimated by HRDI (or underestimated by the MF radars) as previously noticed in HRDI vs. MF radar comparisons. The techniques employed are the conventional t-test applied to the mean values of the paired wind vector components as well as wind speeds, and two nonparametric tests suitable for testing the paired wind speed. The square-root transformation has been applied before the t-tests of the wind speed in order to fit the wind-speed distribution function to the normal distribution. The overall results show little evidence of overestimation by HRDI (underestimation by meteor radars) of wind velocities in the MLT region. Some exceptions are noticed, however, at the altitudes around 88 km, where statistical differences occasionally reach a level of significance of 0.01. The validation is extended to estimate the precision of the wind velocities by both HRDI and meteor radars. In the procedure, the structure function defined by the mean square difference of the observed anomalies is applied in the vertical direction for the profile data. This method assumes the isotropy and the homogeneity of variance for the physical quantity and the homogeneity of variance for the observational errors. The estimated precision is about 6ms–1 for the Shigaraki meteor radar, 15ms–1 for the Jakarta meteor radar, and 20ms–1 for HRDI at 90-km altitude. These values can be used to confirm the statistical significance of the wind field obtained by averaging the observed winds.
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Petracca, M., L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca. "Validation of GPM Dual-Frequency Precipitation Radar (DPR) Rainfall Products over Italy." Journal of Hydrometeorology 19, no. 5 (2018): 907–25. http://dx.doi.org/10.1175/jhm-d-17-0144.1.
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Abstract The Ka–Ku Dual-Frequency Precipitation Radar (DPR) and the Microwave Imager on board the Global Precipitation Measurement (GPM) mission core satellite have been collecting data for more than 3 years, providing precipitation products over the globe, including oceans and remote areas where ground-based precipitation measurements are not available. The main objective of this work is to validate the GPM-DPR products over a key climatic region with complex orography such as the Italian territory. The performances of the DPR precipitation rate products are evaluated over an 18-month period (July 2015–December 2016) using both radar and rain gauge data. The ground reference network is composed of 22 weather radars and more than 3000 rain gauges. DPR dual-frequency products generally show better performance with respect to the single-frequency (i.e., Ka- or Ku-band only) products, especially when ground radar data are taken as reference. A sensitivity analysis with respect to season and rainfall intensity is also carried out. It was found that the normal scan (NS) product outperforms the high-sensitivity scan (HS) and matched scan (MS) during the summer season. A deeper analysis is carried out to investigate the larger discrepancies between the DPR-NS product and ground reference data. The most relevant improvement of the DPR products’ performance was found by limiting the comparison to the upscaled radar data with a higher quality index. The resulting scores in comparison with ground radars are mean error (ME) = −0.44 mm h−1, RMSE = 3.57 mm h−1, and fractional standard error (FSE) = 142%, with the POD = 65% and FAR = 1% for rainfall above 0.5 mm h−1.
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Jiang, Chong, Lin Ren, Jingsong Yang, Qing Xu, and Jinyuan Dai. "Wind Speed Retrieval Using Global Precipitation Measurement Dual-Frequency Precipitation Radar Ka-Band Data at Low Incidence Angles." Remote Sensing 14, no. 6 (2022): 1454. http://dx.doi.org/10.3390/rs14061454.
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In this study, sea surface wind speed was retrieved using the Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) Ka-band data. In order to establish the Ka-band model at low incidence angles, the dependence of the DPR Ka-band normalized radar cross section (NRCS) on the wind speed, incidence angle, sea surface temperature (SST), significant wave height (SWH), and sea surface current speed (CSPD) was analyzed first. We confirmed that the normalized radar cross section depends on the wind speed, incidence angle, and SST. Second, an empirical model at low incidence angles was established. This model links the Ka-band NRCS to the incidence angle, wind speed, and SST. Additionally, the wind speed was retrieved by the model and was validated via the GPM Microwave Imager (GMI) wind product. The validation yielded a root mean square error (RMSE) of 1.45 m/s and the RMSE was better at a lower incidence angle and a higher SST. This model may expand the use of GPM DPR data in enriching the sea surface wind speed data set. It is also helpful for other Ka-band spaceborne radars at low incidence angles to measure wind speed in the future.
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Hayashi, Yoshiaki, Taichi Tebakari, and Akihiro Hashimoto. "A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan." Journal of Disaster Research 16, no. 4 (2021): 786–93. http://dx.doi.org/10.20965/jdr.2021.p0786.
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This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.
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Lee, Yoonjin, Christian D. Kummerow, and Milija Zupanski. "Latent heating profiles from GOES-16 and its impacts on precipitation forecasts." Atmospheric Measurement Techniques 15, no. 23 (2022): 7119–36. http://dx.doi.org/10.5194/amt-15-7119-2022.
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Abstract. Latent heating (LH) is an important factor in both weather forecasting and climate analysis, being the essential factor affecting both the intensity and structure of convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the precipitation radar on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in a lookup table (LUT) that relates instantaneous radar data to corresponding heating profiles. These radars, first on TRMM and then the Global Precipitation Measurement Mission (GPM), provide a continuous record of LH. However, the temporal resolution is too coarse to have significant impacts on forecast models. In operational forecast models such as High-Resolution Rapid Refresh (HRRR), convection is initiated from LH derived from ground-based radars. Despite the high spatial and temporal resolution of ground-based radars, their data are only available over well-observed land areas. This study develops a method to derive LH from the Geostationary Operational Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when formulated as an LUT similar to those used by the TRMM and GPM radars, can successfully be used to derive LH profiles for convective regions based on model simulations with a convective classification scheme and channel 14 (11.2 µm) brightness temperatures. Convective regions detected by GOES-16 are assigned LH profiles from a predefined LUT, and they are compared with LH used by the HRRR model and one of the dual-frequency precipitation radar (DPR) products, the Goddard convective–stratiform heating (CSH). LH obtained from GOES-16 shows similar magnitude to LH derived from the Next Generation Weather Radar (NEXRAD) and CSH, and the vertical distribution of LH is also very similar with CSH. A three-month analysis of total LH from convective clouds from GOES-16 and NEXRAD shows good correlation between the two products. Finally, LH profiles from GOES-16 and NEXRAD are applied to WRF simulations for convective initiation, and their results are compared to investigate their impacts on precipitation forecasts. Results show that LH from GOES-16 has similar impacts to NEXRAD in terms of improving the forecast. While only a proof of concept, this study demonstrates the potential of using LH derived from GOES-16 for convective initialization.
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Mityagina, M. I. "Intensity of convective motions in marine atmospheric boundary layer retrieved from ocean surface radar imagery." Nonlinear Processes in Geophysics 13, no. 3 (2006): 303–8. http://dx.doi.org/10.5194/npg-13-303-2006.
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Abstract. The paper focuses on the occurrence and development of coherent structures observed in the atmosphere above ocean under natural conditions. Microwave imaging radars are suggested as data take instruments. The phenomena of marine atmospheric cells and rolls onset, horizontal planform, aspect ratio and scaling phenomena are examined. Convective patterns manifested in radar images and information derived on the intensity of atmospheric motion are discussed.
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Wingo, Stephanie M., Walter A. Petersen, Patrick N. Gatlin, Charanjit S. Pabla, David A. Marks, and David B. Wolff. "The System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) Precipitation Observation Fusion Framework." Journal of Atmospheric and Oceanic Technology 35, no. 7 (2018): 1353–74. http://dx.doi.org/10.1175/jtech-d-17-0187.1.
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AbstractResearchers now have the benefit of an unprecedented suite of space- and ground-based sensors that provide multidimensional and multiparameter precipitation information. Motivated by NASA’s Global Precipitation Measurement (GPM) mission and ground validation objectives, the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) has been developed as a unique multisensor precipitation data fusion tool to unify field observations recorded in a variety of formats and coordinate systems into a common reference frame. Through platform-specific modules, SIMBA processes data from native coordinates and resolutions only to the extent required to set them into a user-defined three-dimensional grid. At present, the system supports several ground-based scanning research radars, NWS NEXRAD radars, profiling Micro Rain Radars (MRRs), multiple disdrometers and rain gauges, soundings, the GPM Microwave Imager and Dual-Frequency Precipitation Radar on board the Core Observatory satellite, and Multi-Radar Multi-Sensor system quantitative precipitation estimates. SIMBA generates a new atmospheric column data product that contains a concomitant set of all available data from the supported platforms within the user-specified grid defining the column area in the versatile netCDF format. Key parameters for each data source are preserved as attributes. SIMBA provides a streamlined framework for initial research tasks, facilitating more efficient precipitation science. We demonstrate the utility of SIMBA for investigations, such as assessing spatial precipitation variability at subpixel scales and appraising satellite sensor algorithm representation of vertical precipitation structure for GPM Core Observatory overpass cases collected in the NASA Wallops Precipitation Science Research Facility and the GPM Olympic Mountain Experiment (OLYMPEX) ground validation field campaign in Washington State.
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Holt, Benjamin, and F. D. Carsey. "The Separation of Sea-Ice Types in Radar Imagery (Abstract)." Annals of Glaciology 9 (1987): 247. http://dx.doi.org/10.3189/s0260305500000860.
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The ability to distinguish the several major types of sea ice with active radar instruments has been well studied in recent years. The separation of sea-ice types by radar results principally from variations in radar back-scatter due to characteristic differences of these ice types in surface morphology and brine content. When sea ice is viewed with an active radar at angles greater than about 20° from nadir, undeformed ice reflects radar waves and results in a low return, while ridges, hummocks, and small-scale surface features scatter the radar waves and produce a high return. The presence of salt increases the dielectric constant of ice; penetration by radar into the ice is then negligible, and the return is essentially determined by surface morphology. The absence of salt reduces the dielectric properties of ice; radar waves can then penetrate the ice to some depth and are scattered by air bubbles and brine-drainage channels (called volume scattering), thereby enhancing the return even for roughened surfaces. All these properties vary significantly with radar frequency and polarization as well as seasonally. For example, higher radar frequencies respond to smaller-scale surface features, while lower radar frequencies penetrate further into the ice with resulting volume scattering.The high-resolution imagery from synthetic aperture radars (SAR), mounted on aircraft, shuttle, or satellite platforms, is very effective for many sea-ice studies, including the separation of ice types. An aircraft-mounted X-band (9 GHz) SAR, for example, can discriminate smooth first-year ice, rough first-year ice, multi-year ice, and open water by the intensity (tone) of the radar returns and floe geometry. The preferred SARs to date for satellites and shuttle platforms have been L-band (1–2 GHz) systems. SAR imagery of sea ice was extensively acquired by Seasat in 1978 over the Beaufort Sea, with limited quantities obtained by the Shuttle Imaging Radar (SIR-B) over the Weddell Sea in 1984. While L-band SAR can discriminate rough and smooth ice along with roughened open water based on image intensity and floe geometry, the returns from thick first-year ice and multi-year ice are not clearly distinguishable. The fact that there is volume scattering from multi-year ice suggests that there may be textural or spatial frequency variations that could be used to separate these two major ice types in radar imagery. In order to investigate the separation of sea-ice types in the large amount of L-band SAR imagery available, image-analysis techniques including filtering and classification programs have been utilized, pointing towards an automatic classification algorithm for use in future SAR sea-ice data sets, especially from space.An important characteristic of all SAR imagery is the presence of image speckle, a coherent form of noise caused by the random variability of scatterers across even a uniform surface. Most SAR processors reduce this effect by averaging multiple independent samples but this is done at the cost of reducing resolution. Speckle reduction can also be accomplished by filtering. Several filters have been tested including median, box, and adaptive edge filters. Each filter has different characteristics in terms of smoothing speckle and in the response to sharp gradients or edges, such as ridge or lead openings, as well as computational requirements. Optimization of each filter’s parameters has been determined by the quality of classification of each ice type.The classification programs that have been tested are based on tone and texture image characteristics. The programs are supervised; that is, a small training area for each class is pre-selected for statistical analysis. From these statistics, the remainder of the imagery is subjected to the particular classification algorithm. The tone program separates classes based on the mean, standard deviation, and number of standard deviations of each class, and includes a Bayesian maximum-likelihood classifier for ambiguous elements. The texture program determines the statistical homogeneity of each class and the optimal segmentation of each small area into the various classes.
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Holt, Benjamin, and F. D. Carsey. "The Separation of Sea-Ice Types in Radar Imagery (Abstract)." Annals of Glaciology 9 (1987): 247. http://dx.doi.org/10.1017/s0260305500000860.
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The ability to distinguish the several major types of sea ice with active radar instruments has been well studied in recent years. The separation of sea-ice types by radar results principally from variations in radar back-scatter due to characteristic differences of these ice types in surface morphology and brine content. When sea ice is viewed with an active radar at angles greater than about 20° from nadir, undeformed ice reflects radar waves and results in a low return, while ridges, hummocks, and small-scale surface features scatter the radar waves and produce a high return. The presence of salt increases the dielectric constant of ice; penetration by radar into the ice is then negligible, and the return is essentially determined by surface morphology. The absence of salt reduces the dielectric properties of ice; radar waves can then penetrate the ice to some depth and are scattered by air bubbles and brine-drainage channels (called volume scattering), thereby enhancing the return even for roughened surfaces. All these properties vary significantly with radar frequency and polarization as well as seasonally. For example, higher radar frequencies respond to smaller-scale surface features, while lower radar frequencies penetrate further into the ice with resulting volume scattering. The high-resolution imagery from synthetic aperture radars (SAR), mounted on aircraft, shuttle, or satellite platforms, is very effective for many sea-ice studies, including the separation of ice types. An aircraft-mounted X-band (9 GHz) SAR, for example, can discriminate smooth first-year ice, rough first-year ice, multi-year ice, and open water by the intensity (tone) of the radar returns and floe geometry. The preferred SARs to date for satellites and shuttle platforms have been L-band (1–2 GHz) systems. SAR imagery of sea ice was extensively acquired by Seasat in 1978 over the Beaufort Sea, with limited quantities obtained by the Shuttle Imaging Radar (SIR-B) over the Weddell Sea in 1984. While L-band SAR can discriminate rough and smooth ice along with roughened open water based on image intensity and floe geometry, the returns from thick first-year ice and multi-year ice are not clearly distinguishable. The fact that there is volume scattering from multi-year ice suggests that there may be textural or spatial frequency variations that could be used to separate these two major ice types in radar imagery. In order to investigate the separation of sea-ice types in the large amount of L-band SAR imagery available, image-analysis techniques including filtering and classification programs have been utilized, pointing towards an automatic classification algorithm for use in future SAR sea-ice data sets, especially from space. An important characteristic of all SAR imagery is the presence of image speckle, a coherent form of noise caused by the random variability of scatterers across even a uniform surface. Most SAR processors reduce this effect by averaging multiple independent samples but this is done at the cost of reducing resolution. Speckle reduction can also be accomplished by filtering. Several filters have been tested including median, box, and adaptive edge filters. Each filter has different characteristics in terms of smoothing speckle and in the response to sharp gradients or edges, such as ridge or lead openings, as well as computational requirements. Optimization of each filter’s parameters has been determined by the quality of classification of each ice type. The classification programs that have been tested are based on tone and texture image characteristics. The programs are supervised; that is, a small training area for each class is pre-selected for statistical analysis. From these statistics, the remainder of the imagery is subjected to the particular classification algorithm. The tone program separates classes based on the mean, standard deviation, and number of standard deviations of each class, and includes a Bayesian maximum-likelihood classifier for ambiguous elements. The texture program determines the statistical homogeneity of each class and the optimal segmentation of each small area into the various classes.
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Costanzo, Sandra, Giuseppe Di Massa, Antonio Costanzo, et al. "Multimode/Multifrequency Low Frequency Airborne Radar Design." Journal of Electrical and Computer Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/857530.
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This work deals with the design of multimode/multifrequency airborne radar suitable for imaging and subsurface sounding. The system operates at relatively low frequencies in the band ranging from VHF to UHF. It is able to work in two different modalities: (i) nadir-looking sounder in the VHF band (163 MHz) and (ii) side-looking imager (SAR) in the UHF band with two channels at 450 MHz and 860 MHz. The radar has been completely designed by CO.Ri.S.T.A. for what concerns the RF and the electronic aspect, and by the University of Calabria for what concerns the design, realization, and test of SAR antennas. The radar has been installed on a civil helicopter and its operation has been validated in flight in both sounder and imager modality. Preliminary surveys have been carried out over different areas of Campania region, South Italy.
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Lee, Yoonjin, Christian D. Kummerow, and Milija Zupanski. "A simplified method for the detection of convection using high-resolution imagery from GOES-16." Atmospheric Measurement Techniques 14, no. 5 (2021): 3755–71. http://dx.doi.org/10.5194/amt-14-3755-2021.
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Abstract. The ability to detect convective regions and to add latent heating to drive convection is one of the most important additions to short-term forecast models such as National Oceanic and Atmospheric Administration's (NOAA's) High-Resolution Rapid Refresh (HRRR) model. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and infrared sensors on a geostationary satellite can provide data that are more sensitive to small droplets, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, Geostationary Operational Environmental Satellite-16 and Satellite-17 (GOES-16 and GOES-17), along with Himawari-8, can make up for this lack of vertical information through the use of very high spatial and temporal resolutions, allowing better observation of bubbling features on convective cloud tops. This study develops two algorithms to detect convection at vertically growing clouds and mature convective clouds using 1 min GOES-16 Advanced Baseline Imager (ABI) data. Two case studies are used to explain the two methods, followed by results applied to 1 month of data over the contiguous United States. Vertically growing clouds in early stages are detected using decreases in brightness temperatures over 10 min. For mature convective clouds which no longer show much of a decrease in brightness temperature, the lumpy texture from rapid development can be observed using 1 min high spatial resolution reflectance data. The detection skills of the two methods are validated against Multi-Radar/Multi-Sensor System (MRMS), a ground-based radar product. With the contingency table, results applying both methods to 1-month data show a relatively low false alarm rate of 14.4 % but missed 54.7 % of convective clouds detected by the radar product. These convective clouds were missed largely due to less lumpy texture, which is mostly caused by optically thick cloud shields above.
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Montopoli, M., G. Vulpiani, D. Cimini, E. Picciotti, and F. S. Marzano. "Interpretation of observed microwave signatures from ground dual polarization radar and space multi frequency radiometer for the 2011 Grímsvötn volcanic eruption." Atmospheric Measurement Techniques Discussions 6, no. 4 (2013): 6215–48. http://dx.doi.org/10.5194/amtd-6-6215-2013.
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Abstract. The important role played by ground-based microwave weather radars for the monitoring of volcanic ash clouds has been recently demonstrated. The potential of microwaves from satellite passive and ground-based active sensors to estimate near-source volcanic ash cloud parameters has been also proposed, though with little investigation of their synergy and the role of the radar polarimetry. The goal of this work is to show the potentiality and drawbacks of the X-band Dual Polarization radar measurements (DPX) through the data acquired during the latest Grímsvötn volcanic eruptions that took place on May 2011 in Iceland. The analysis is enriched by the comparison between DPX data and the observations from the satellite Special Sensor Microwave Imager/Sounder (SSMIS) and a C-band Single Polarization (SPC) radar. SPC, DPX, and SSMIS instruments cover a large range of the microwaves spectrum, operating respectively at 5.4, 3.2, and 0.16–1.6 cm wavelengths. The multi-source comparison is made in terms of Total Columnar Concentration (TCC). The latter is estimated from radar observables using the "Volcanic Ash Radar Retrieval for dual-Polarization X band systems" (VARR-PX) algorithm and from SSMIS brightness temperature (BT) using a linear BT–TCC relationship. The BT–TCC relationship has been compared with the analogous relation derived from SSMIS and SPC radar data for the same case study. Differences between these two linear regression curves are mainly attributed to an incomplete observation of the vertical extension of the ash cloud, a coarser spatial resolution and a more pronounced non-uniform beam filling effect of SPC measurements (260 km far from the volcanic vent) with respect to the DPX (70 km from the volcanic vent). Results show that high-spatial-resolution DPX radar data identify an evident volcanic plume signature, even though the interpretation of the polarimetric variables and the related retrievals is not always straightforward, likely due to the possible formation of ash and ice particle aggregates and the radar signal depolarization induced by turbulence effects. The correlation of the estimated TCCs derived from DPX and SSMIS BTs reaches −0.73.
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Lee, Kangjin, Seong-Gyeong Jeon, Seok-Yong Seong, and Ki-mook Kang. "Technology Trend in Synthetic Aperture Radar (SAR) Imagery Analysis Tools." Journal of Space Technology and Applications 1, no. 2 (2021): 268–81. http://dx.doi.org/10.52912/jsta.2021.1.2.268.
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Shige, Shoichi, Satoshi Kida, Hiroki Ashiwake, Takuji Kubota, and Kazumasa Aonashi. "Improvement of TMI Rain Retrievals in Mountainous Areas." Journal of Applied Meteorology and Climatology 52, no. 1 (2013): 242–54. http://dx.doi.org/10.1175/jamc-d-12-074.1.
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AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.
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Hysell, D. L., M. F. Larsen, and Q. H. Zhou. "Common volume coherent and incoherent scatter radar observations of mid-latitude sporadic E-layers and QP echoes." Annales Geophysicae 22, no. 9 (2004): 3277–90. http://dx.doi.org/10.5194/angeo-22-3277-2004.
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Abstract. Common-volume observations of sporadic E-layers made on 14-15 June 2002 with the Arecibo incoherent scatter radar and a 30MHz coherent scatter radar imager located on St. Croix are described. Operating in dual-beam mode, the Arecibo radar detected a slowly descending sporadic E-layer accompanied by a series of dense E-region plasma clouds at a time when the coherent scatter radar was detecting quasi-periodic (QP) echoes. Using coherent radar imaging, we collocate the sources of the coherent scatter with the plasma clouds observed by Arecibo. In addition to patchy, polarized scattering regions drifting through the radar illuminated volume, which have been observed in previous imaging experiments, the 30MHz radar also detected large-scale electrostatic waves in the E-region over Puerto Rico, with a wavelength of about 30km and a period of about 10min, propagating to the southwest. Both the intensity and the Doppler shifts of the coherent echoes were modulated by the wave.
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Stapleton, N. R. "Ship wakes in radar imagery." International Journal of Remote Sensing 18, no. 6 (1997): 1381–86. http://dx.doi.org/10.1080/014311697218494.
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Abidin Ismail, Z. "Radar Imagery Monitored Crop Identification." IFAC Proceedings Volumes 34, no. 11 (2001): 303–5. http://dx.doi.org/10.1016/s1474-6670(17)34153-8.
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Pitkänen, T., A. T. Aikio, A. Kozlovsky, and O. Amm. "Reconnection electric field estimates and dynamics of high-latitude boundaries during a substorm." Annales Geophysicae 27, no. 5 (2009): 2157–71. http://dx.doi.org/10.5194/angeo-27-2157-2009.
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Abstract. The dynamics of the polar cap and the auroral oval are examined in the evening sector during a substorm period on 25 November 2000 by using measurements of the EISCAT incoherent scatter radars, the north-south chain of the MIRACLE magnetometer network, and the Polar UV Imager. The location of the polar cap boundary (PCB) is estimated from electron temperature measurements by the mainland low-elevation EISCAT VHF radar and the 42 m antenna of the EISCAT Svalbard radar. A comparison to the poleward auroral emission (PAE) boundary by the Polar UV Imager shows that in this event the PAE boundary is typically located 0.7° of magnetic latitude poleward of the PCB by EISCAT. The convection reversal boundary (CRB) is determined from the 2-D plasma drift velocity extracted from the dual-beam VHF data. The CRB is located 0.5–1° equatorward of the PCB indicating the existence of viscous-driven antisunward convection on closed field lines. East-west equivalent electrojets are calculated from the MIRACLE magnetometer data by the 1-D upward continuation method. In the substorm growth phase, electrojets together with the polar cap boundary move gradually equatorwards. During the substorm expansion phase, the Harang discontinuity (HD) region expands to the MLT sector of EISCAT. In the recovery phase the PCB follows the poleward edge of the westward electrojet. The local ionospheric reconnection electric field is calculated by using the measured plasma velocities in the vicinity of the polar cap boundary. During the substorm growth phase, values between 0 and 10 mV/m are found. During the late expansion and recovery phase, the reconnection electric field has temporal variations with periods of 7–27 min and values from 0 to 40 mV/m. It is shown quantitatively, for the first time to our knowledge, that intensifications in the local reconnection electric field correlate with appearance of auroral poleward boundary intensifications (PBIs) in the same MLT sector. The results suggest that PBIs (typically 1.5 h MLT wide) are a consequence of temporarily enhanced longitudinally localized magnetic flux closure in the magnetotail.
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Danilicheva, O. A., S. A. Ermakov, and I. A. Kapustin. "Retrieval of surface currents from sequential satellite radar images." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 17, no. 6 (2020): 93–96. http://dx.doi.org/10.21046/2070-7401-2020-17-6-93-96.
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Gatlin, Patrick N., Walter A. Petersen, Jason L. Pippitt, Todd A. Berendes, David B. Wolff, and Ali Tokay. "The GPM Validation Network and Evaluation of Satellite-Based Retrievals of the Rain Drop Size Distribution." Atmosphere 11, no. 9 (2020): 1010. http://dx.doi.org/10.3390/atmos11091010.
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A unique capability of the Global Precipitation Measurement (GPM) mission is its ability to better estimate the raindrop size distribution (DSD) on a global scale. To validate the GPM DSD retrievals, a network of more than 100 ground-based polarimetric radars from across the globe are utilized within the broader context of the GPM Validation Network (VN) processing architecture. The GPM VN ensures quality controlled dual-polarimetric radar moments for use in providing reference estimates of the DSD. The VN DSD estimates are carefully geometrically matched with the GPM core satellite measurements for evaluation of the GPM algorithms. We use the GPM VN to compare the DSD retrievals from the GPM’s Dual-frequency Precipitation Radar (DPR) and combined DPR–GPM Microwave Imager (GMI) Level-2 algorithms. Results suggested that the Version 06A GPM core satellite algorithms provide estimates of the mass-weighted mean diameter (Dm) that are biased 0.2 mm too large when considered across all precipitation types. In convective precipitation, the algorithms tend to overestimate Dm by 0.5–0.6 mm, leading the DPR algorithm to underestimate the normalized DSD intercept parameter (Nw) by a factor of two, and introduce a significant bias to the DPR retrievals of rainfall rate for DSDs with large Dm. The GPM Combined algorithm performs better than the DPR algorithm in convection but provides a severely limited range of Nw estimates, highlighting the need to broaden its a priori database in convective precipitation.
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Lee, Yoonjin, Christian D. Kummerow, and Imme Ebert-Uphoff. "Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data." Atmospheric Measurement Techniques 14, no. 4 (2021): 2699–716. http://dx.doi.org/10.5194/amt-14-2699-2021.
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Abstract. An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.
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Cheng, Yi-Lin, Wen-Hsiang Yeh, and Yu-Ping Liao. "The Implementation of a Gesture Recognition System with a Millimeter Wave and Thermal Imager." Sensors 24, no. 2 (2024): 581. http://dx.doi.org/10.3390/s24020581.
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During the COVID-19 pandemic, the number of cases continued to rise. As a result, there was a growing demand for alternative control methods to traditional buttons or touch screens. However, most current gesture recognition technologies rely on machine vision methods. However, this method can lead to suboptimal recognition results, especially in situations where the camera is operating in low-light conditions or encounters complex backgrounds. This study introduces an innovative gesture recognition system for large movements that uses a combination of millimeter wave radar and a thermal imager, where the multi-color conversion algorithm is used to improve palm recognition on the thermal imager together with deep learning approaches to improve its accuracy. While the user performs gestures, the mmWave radar captures point cloud information, which is then analyzed through neural network model inference. It also integrates thermal imaging and palm recognition to effectively track and monitor hand movements on the screen. The results suggest that this combined method significantly improves accuracy, reaching a rate of over 80%.
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Esmaeilzade, M., J. Amini, and S. Zakeri. "GEOREFERENCING ON SYNTHETIC APERTURE RADAR IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 179–84. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-179-2015.
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Due to the SAR<sup>1</sup> geometry imaging, SAR images include geometric distortions that would be erroneous image information and the images should be geometrically calibrated. As the radar systems are side looking, geometric distortion such as shadow, foreshortening and layover are occurred. To compensate these geometric distortions, information about sensor position, imaging geometry and target altitude from ellipsoid should be available. In this paper, a method for geometric calibration of SAR images is proposed. The method uses Range-Doppler equations. In this method, for the image georeferencing, the DEM<sup>2</sup> of SRTM with 30m pixel size is used and also exact ephemeris data of the sensor is required. In the algorithm proposed in this paper, first digital elevation model transmit to range and azimuth direction. By applying this process, errors caused by topography such as foreshortening and layover are removed in the transferred DEM. Then, the position of the corners on original image is found base on the transferred DEM. Next, original image registered to transfer DEM by 8 parameters projective transformation. The output is the georeferenced image that its geometric distortions are removed. The advantage of the method described in this article is that it does not require any control point as well as the need to attitude and rotational parameters of the sensor. Since the ground range resolution of used images are about 30m, the geocoded images using the method described in this paper have an accuracy about 20m (subpixel) in planimetry and about 30m in altimetry. <br><br> <sup>1</sup> Synthetic Aperture Radar <br> <sup>2</sup> Digital Elevation Model
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Chen, Sei-Wang, and Anil K. Jain. "Object extraction from laser radar imagery." Pattern Recognition 24, no. 6 (1991): 587–600. http://dx.doi.org/10.1016/0031-3203(91)90024-y.
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Lin, Xin, and Arthur Y. Hou. "Evaluation of Coincident Passive Microwave Rainfall Estimates Using TRMM PR and Ground Measurements as References." Journal of Applied Meteorology and Climatology 47, no. 12 (2008): 3170–87. http://dx.doi.org/10.1175/2008jamc1893.1.
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Abstract This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h−1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h−1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates.
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Prikryl, P., J. W. MacDougall, I. F. Grant, D. P. Steele, G. J. Sofko, and R. A. Greenwald. "Observations of polar patches generated by solar wind Alfvén wave coupling to the dayside magnetosphere." Annales Geophysicae 17, no. 4 (1999): 463–89. http://dx.doi.org/10.1007/s00585-999-0463-0.
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Abstract. A long series of polar patches was observed by ionosondes and an all-sky imager during a disturbed period (Kp = 7- and IMF Bz < 0). The ionosondes measured electron densities of up to 9 × 1011 m-3 in the patch center, an increase above the density minimum between patches by a factor of \\sim4.5. Bands of F-region irregularities generated at the equatorward edge of the patches were tracked by HF radars. The backscatter bands were swept northward and eastward across the polar cap in a fan-like formation as the afternoon convection cell expanded due to the IMF By > 0. Near the north magnetic pole, an all-sky imager observed the 630-nm emission patches of a distinctly band-like shape drifting northeastward to eastward. The 630-nm emission patches were associated with the density patches and backscatter bands. The patches originated in, or near, the cusp footprint where they were formed by convection bursts (flow channel events, FCEs) structuring the solar EUV-produced photoionization and the particle-produced auroral/cusp ionization by segmenting it into elongated patches. Just equatorward of the cusp footprint Pc5 field line resonances (FLRs) were observed by magnetometers, riometers and VHF/HF radars. The AC electric field associated with the FLRs resulted in a poleward-progressing zonal flow pattern and backscatter bands. The VHF radar Doppler spectra indicated the presence of steep electron density gradients which, through the gradient drift instability, can lead to the generation of the ionospheric irregularities found in patches. The FLRs and FCEs were associated with poleward-progressing DPY currents (Hall currents modulated by the IMF By) and riometer absorption enhancements. The temporal and spatial characteristics of the VHF backscatter and associated riometer absorptions closely resembled those of poleward moving auroral forms (PMAFs). In the solar wind, IMP 8 observed large amplitude Alfvén waves that were correlated with Pc5 pulsations observed by the ground magnetometers, riometers and radars. It is concluded that the FLRs and FCEs that produced patches were driven by solar wind Alfvén waves coupling to the dayside magnetosphere. During a period of southward IMF the dawn-dusk electric field associated with the Alfvén waves modulated the subsolar magnetic reconnection into pulses that resulted in convection flow bursts mapping to the ionospheric footprint of the cusp.Key words. Ionosphere (polar ionosphere). Magneto- spheric physics (magnetosphere-ionosphere interactions; polar wind-magnetosphere interactions).
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Agram, Piyush S., Michael S. Warren, Scott A. Arko, and Matthew T. Calef. "Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery." Remote Sensing 15, no. 7 (2023): 1932. http://dx.doi.org/10.3390/rs15071932.
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We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We have quantified the loss of precision due to the application of static flattening factors, and show that these are well below the stated requirements of change-detection algorithms. Finally, we have discussed the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth-Observation Satellites (CEOS) Analysis-Ready Dataset (ARD) families—Geocoded Single-Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework.
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Zhang, Yuansheng, Dongjie Cao, Jing Yang, et al. "A Parallax Shift Effect Correction Based on Cloud Top Height for FY-4A Lightning Mapping Imager (LMI)." Remote Sensing 15, no. 19 (2023): 4856. http://dx.doi.org/10.3390/rs15194856.
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The Lightning Mapping Imager (LMI) onboard the Fengyun-4A (FY-4A) satellite is the first independently developed satellite-borne lightning imager in China. It enables continuous lightning detection in China and surrounding areas, regardless of weather conditions. The FY-4A LMI uses a Charge-Coupled Device (CCD) array for lightning detection, and the accuracy of lightning positioning is influenced by cloud top height (CTH). In this study, we proposed an ellipsoid CTH parallax correction (ECPC) model for lightning positioning applicable to FY-4A LMI. The model utilizes CTH data from the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A to correct the lightning positioning data. According to the model, when the CTH is 12 km, the maximum deviation in lightning positioning caused by CTH in Beijing is approximately 0.1177° in the east–west direction and 0.0530° in the north–south direction, corresponding to a horizontal deviation of 13.1558 km, which exceeds the size of a single ground detection unit of the geostationary satellite lightning imager. Therefore, it is necessary to be corrected. A comparison with data from the Beijing Broadband Lightning Network (BLNET) and radar data shows that the corrected LMI data exhibit spatial distribution that is closer to the simultaneous BLNET lightning positioning data. The coordinate differences between the two datasets are significantly reduced, indicating higher consistency with radar data. The correction algorithm decreases the LMI lightning location deviation caused by CTH, thereby improving the accuracy and reliability of satellite lightning positioning data. The proposed ECPC model can be used for the real-time correction of lightning data when CTH is obtained at the same time, and it can be also used for the post-correction of space-based lightning detection with other cloud top height data.
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Marzano, F. S., S. Mori, M. Chini, et al. "Potential of high-resolution detection and retrieval of precipitation fields from X-band spaceborne synthetic aperture radar over land." Hydrology and Earth System Sciences 15, no. 3 (2011): 859–75. http://dx.doi.org/10.5194/hess-15-859-2011.
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Abstract. X-band Synthetic Aperture Radars (X-SARs), able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about few hundred meters, due to the atmospheric moving-target degradation effects. This capability has become very appealing due to the recent launch of several X-SAR satellites, even though several remote sensing issues are still open. This work is devoted to: (i) explore the potential of X-band high-resolution detection and retrieval of rainfall fields from space using X-SAR signal backscattering amplitude and interferometric phase; (ii) evaluate the effects of spatial resolution degradation by precipitation and inhomogeneous beam filling when comparing to other satellite-based sensors. Our X-SAR analysis of precipitation effects has been carried out using both a TerraSAR-X (TSX) case study of Hurricane "Gustav" in 2008 over Mississippi (USA) and a COSMO-SkyMed (CSK) X-SAR case study of orographic rainfall over Central Italy in 2009. For the TSX case study the near-surface rain rate has been retrieved from the normalized radar cross section by means of a modified regression empirical algorithm (MREA). A relatively simple method to account for the geometric effect of X-SAR observation on estimated rainfall rate and first-order volumetric effects has been developed and applied. The TSX-retrieved rain fields have been compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (AL, USA). The rainfall detection capability of X-SAR has been tested on the CSK case study using the repeat-pass coherence response and qualitatively comparing its signature with ground-based Mt. Midia C-band radar in central Italy. A numerical simulator to represent the effect of the spatial resolution and the antenna pattern of TRMM satellite Precipitation Radar (PR) and Microwave Imager (TMI), using high-resolution TSX-retrieved rain images, has been also set up in order to evaluate the rainfall beam filling phenomenon. As expected, the spatial average can modify the statistics of the high-resolution precipitation fields, strongly reducing its dynamics in a way non-linearly dependent on the rain rate local average value.
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Marzano, F. S., S. Mori, M. Chini, et al. "Potential of high-resolution detection and retrieval of precipitation fields from X-band spaceborne Synthetic Aperture Radar over land." Hydrology and Earth System Sciences Discussions 7, no. 5 (2010): 7451–84. http://dx.doi.org/10.5194/hessd-7-7451-2010.
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Abstract. X-band Synthetic Aperture Radars (X-SARs), able to image the Earth's surface at metric resolution, may provide a unique opportunity to measure rainfall over land with spatial resolution of about few hundred meters, due to the atmospheric moving-target degradation effects. This capability has become very appealing due to the recent launch of several X-SAR satellites, even though several remote sensing issues are still open. This work is devoted to: (i) explore the potential of X-band high-resolution detection and retrieval of rainfall fields from space using X-SAR signal backscattering amplitude and interferometric phase; (ii) evaluate the effects of spatial resolution degradation by precipitation and inhomogeneous beam filling when comparing to other satellite-based sensors. Our X-SAR analysis of precipitation effects has been carried out using both a TerraSAR-X (TSX) case study of Hurricane "Gustav" in 2008 over Mississippi (USA) and a COSMO-SkyMed (CSK) X-SAR case study of orographic rainfall over Central Italy in 2009. For the TSX case study the near-surface rain rate has been retrieved from the normalized radar cross section by means of a modified regression empirical algorithm (MREA). A relatively simple method to account for the geometric effect of X-SAR observation on estimated rainfall rate and first-order volumetric effects has been developed and applied. The TSX-retrieved rain fields have been compared to those estimated from the Next Generation Weather Radar (NEXRAD) in Mobile (AL, USA). The rainfall detection capability of X-SAR has been tested on the CSK case study using the repeat-pass coherence response and qualitatively comparing its signature with ground-based Mt. Midia C-band radar in central Italy. A numerical simulator to represent the effect of the spatial resolution and the antenna pattern of TRMM satellite Precipitation Radar (PR) and Microwave Imager (TMI), using high-resolution TSX-retrieved rain images, has been also set up in order to evaluate the rainfall beam filling phenomenon. As expected, the spatial average can modify the statistics of the high-resolution precipitation fields, strongly reducing its dynamics in a way non-linearly dependent on the rainrate local average value.
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Matthee, Retha, John R. Mecikalski, Lawrence D. Carey, and Phillip M. Bitzer. "Quantitative Differences between Lightning and Nonlightning Convective Rainfall Events as Observed with Polarimetric Radar and MSG Satellite Data." Monthly Weather Review 142, no. 10 (2014): 3651–65. http://dx.doi.org/10.1175/mwr-d-14-00047.1.
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Abstract To increase understanding of the relationships between lightning and nonlightning convective storms, lightning observations from the National Aeronautics and Space Administration (NASA) African Monsoon Multidisciplinary Analyses (NAMMA) campaign were analyzed with Meteosat Second Generation (MSG) geostationary satellite and S-band NASA Polarimetric Doppler Weather Radar (NPOL) data. The study’s goal was to analyze the time evolution of infrared satellite fields and ground-based polarimetric radar during NAMMA to quantify relationships between satellite and radar observations for lightning and nonlightning convective clouds over equatorial Africa. Using NPOL data, very low-frequency arrival time difference lightning data, and MSG Spinning Enhanced Visible and Infrared Imager observations, the physical attributes of growing cumulus clouds, including ice mass production, updraft strength, cloud depth, and cloud-top glaciation were examined. It was found that, on average, the lightning storms had stronger updrafts (seen in the satellite and radar fields), which lead to the formation of deeper clouds (seen in the satellite and radar fields) and subsequently much more ice in the mixed-phase region (as confirmed in radar observations), as well as much more nonprecipitating ice in the top 1 km of the cloud (as quantified in both satellite and radar fields) than the nonlightning storms. Computed radar-derived ice masses in cumulus clouds verifies the traditional MSG indicators of cloud-top glaciation, while NPOL verifies internal structures (i.e., large amounts of graupel) where satellite and radar show strong updrafts.
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Gordon, Samantha, and Graham Brooker. "Using Schlieren Imaging and a Radar Acoustic Sounding System for the Detection of Close-in Air Turbulence." Sensors 23, no. 19 (2023): 8255. http://dx.doi.org/10.3390/s23198255.
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This paper presents a novel sensor for the detection and characterization of regions of air turbulence. As part of the ground truth process, it consists of a combined Schlieren imager and a Radar Acoustic Sounding System (RASS) to produce dual-modality “images” of air movement within the measurement volume. The ultrasound-modulated Schlieren imager consists of a strobed point light source, parabolic mirror, light block, and camera, which are controlled by two laptops. It provides a fine-scale projection of the acoustic pulse-modulated air turbulence through the measurement volume. The narrow beam 40 kHz/17 GHz RASS produces spectra based on Bragg-enhanced Doppler radar reflections from the acoustic pulse as it travels. Tests using artificially generated air vortices showed some disruption of the Schlieren image and of the RASS spectrogram. This should allow the higher-resolution Schlieren images to identify the turbulence mechanisms that are disrupting the RASS spectra. The objective of this combined sensor is to have the Schlieren component inform the interpretation of RASS spectra to allow the latter to be used as a stand-alone sensor on a UAV.
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Veillette, Mark S., Eric P. Hassey, Christopher J. Mattioli, Haig Iskenderian, and Patrick M. Lamey. "Creating Synthetic Radar Imagery Using Convolutional Neural Networks." Journal of Atmospheric and Oceanic Technology 35, no. 12 (2018): 2323–38. http://dx.doi.org/10.1175/jtech-d-18-0010.1.
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AbstractIn this work deep convolutional neural networks (CNNs) are shown to be an effective model for fusing heterogeneous geospatial data to create radar-like analyses of precipitation intensity (i.e., synthetic radar). The CNN trained in this work has a directed acyclic graph (DAG) structure that takes inputs from multiple data sources with varying spatial resolutions. These data sources include geostationary satellite (1-km visible and four 4-km infrared bands), lightning flash density from Earth Network’s Total Lightning Network, and numerical model data from NOAA’s 13-km Rapid Refresh model. A regression is performed in the final layer of the network using NEXRAD-derived data mapped onto a 1-km grid as a target variable. The outputs of the CNN are fused with analyses from NEXRAD to create seamless radar mosaics that extend to offshore sectors and beyond. The model is calibrated and validated using both NEXRAD and spaceborne radar from NASA’s Global Precipitation Measurement (GPM) Mission’s Core Observatory satellite. The advantages over a random forest–based approach used in previous works are discussed.
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Aruliah, A. L., E. M. Griffin, H. C. I. Yiu, I. McWhirter, and A. Charalambous. "SCANDI – an all-sky Doppler imager for studies of thermospheric spatial structure." Annales Geophysicae 28, no. 2 (2010): 549–67. http://dx.doi.org/10.5194/angeo-28-549-2010.
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Abstract. A new all-sky Fabry-Perot Interferometer called the Scanning Doppler Imager (SCANDI) was built and installed at Longyearbyen in December 2006. Observations have been made of the Doppler shifts and Doppler broadening of the 630 nm airglow and aurora, from which upper thermospheric winds and temperatures are calculated. SCANDI allows measurements over a field-of-view (FOV) with a horizontal radius of nearly 600 km for observations at an altitude of 250 km using a time resolution of 8 min. The instrument provides the ability to observe thermospheric spatial structure within a FOV which overlaps that of the EISCAT Svalbard radar and CUTLASS SuperDARN radars. Coordinating with these instruments provides an important opportunity for studying ion-neutral coupling. The all-sky image is divided into several sectors to provide a horizontal spatial resolution of between 100–300 km. This is a powerful extension in observational capability but requires careful calibration and data analysis, as described here. Two observation modes were used: a fixed and a scanning etalon gap. SCANDI results are corroborated using the Longyearbyen single look direction FPI, and ESR measurements of the ion temperatures. The data show thermospheric temperature gradients of a few Kelvins per kilometre, and a great deal of meso-scale variability on spatial scales of several tens of kilometres.
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Kim, Ji-Hye, Mi-Lim Ou, Jun-Dong Park, Kenneth R. Morris, Mathew R. Schwaller, and David B. Wolff. "Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula." Journal of Atmospheric and Oceanic Technology 31, no. 9 (2014): 1902–21. http://dx.doi.org/10.1175/jtech-d-13-00193.1.
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Abstract Since 2009, the Korea Meteorological Administration (KMA) has participated in ground validation (GV) projects through international partnerships within the framework of the Global Precipitation Measurement (GPM) Mission. The goal of this work is to assess the reliability of ground-based measurements in the Korean Peninsula as a means for validating precipitation products retrieved from satellite microwave sensors, with an emphasis on East Asian precipitation. KMA has a well-developed operational weather service infrastructure composed of meteorological radars, a dense rain gauge network, and automated weather stations. Measurements from these systems, including data from four ground-based radars (GRs), were combined with satellite data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and used as a proxy for GPM GV over the Korean Peninsula. A time series of mean reflectivity differences (GR − PR) for stratiform-only and above-brightband-only data showed that the time-averaged difference fell between −2.0 and +1.0 dBZ for the four GRs used in this study. Site-specific adjustments for these relative mean biases were applied to GR reflectivities, and detailed statistical comparisons of reflectivity and rain rate between PR and bias-adjusted GR were carried out. In rain-rate comparisons, surface rain from the TRMM Microwave Imager (TMI) and the rain gauges were added and the results varied according to rain type. Bias correction has had a positive effect on GR rain rate comparing with PR and gauge rain rates. This study confirmed advance preparation for GPM GV system was optimized on the Korean Peninsula using the official framework.
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Kataoka, R., H. Fukunishi, K. Hosokawa, et al. "Transient production of F-region irregularities associated with TCV passage." Annales Geophysicae 21, no. 7 (2003): 1531–41. http://dx.doi.org/10.5194/angeo-21-1531-2003.
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Abstract. Transient production of F-region plasma irregularities due to traveling convection vortices (TCVs) was investigated using the Super Dual Auroral Radar Network (SuperDARN) combined with ground magnetometer networks and the POLAR ultraviolet imager. We selected two large-amplitude (100–200 nT) TCV events that occurred on 22 May 1996 and 24 July 1996. It is found that the TCV-associated HF backscatter arises in blobs with spatial scale of a few hundreds km. They traveled following tailward bulk motion of the TCV across the three fields-of-view of the SuperDARN HF radars in the prenoon sector. The spectra in the blobs showed unidirectional Doppler velocities of typically 400–600 m/s, with flow directions away from the radar. These unidirectional velocities correspond to the poleward and/or eastward convective flow near the leading edge of upward field-aligned current. The backscatter blobs overlapped the poleward and westward part of the TCV-related transient aurora. It is likely that the transient backscatter blobs are produced by the three-dimensional gradient drift instabilities in the three-dimensional current system of the TCV. In this case, nonlinear rapid evolution of irregularities would occur in the upward field-aligned current region. The spectral width of the backscatter blob is typically distributed between 50 and 300 m/s, but sometimes it is over 400 m/s. This suggests that the temporal broad spectra over 400 m/s are produced by Pc1–2 bursts, while the background spectral width of 50–300 m/s are produced by the velocity gradient structure of convection vortices themselves.Key words. Ionosphere (Electric fields and currents; Ionospheric irregularities; Plasma convection)
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Hollaus, Markus, and Mariette Vreugdenhil. "Radar Satellite Imagery for Detecting Bark Beetle Outbreaks in Forests." Current Forestry Reports 5, no. 4 (2019): 240–50. http://dx.doi.org/10.1007/s40725-019-00098-z.
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Abstract Purpose of Review The overall objective of this paper is to review the state of knowledge on the application of radar data for detecting bark beetle attacks in forests. Due to the increased availability of high spatial and temporal resolution radar data (e.g. Sentinel-1 (S1)), the question is how this time series data can support operational forest management with respect to forest insect damage prevention. Furthermore, available radar systems will be listed and their potential for detecting bark beetle attacks will be discussed. To increase the understanding of the potential of radar time series for detecting bark beetle outbreaks, a theoretical background about the interaction of the radar signals with the forest canopy is given. Finally, gaps in the available knowledge are identified and future research questions are formulated which could advance our understanding of using radar data for detecting forest bark beetle attacks. Recent Findings Few studies already demonstrate the high potential of S1 time series data for forest disturbance mapping in general. It was demonstrated that multi-temporal S1 data provide an excellent data source of describing the phenological characteristics of forests, which provide the basic knowledge for detecting bark beetle induced forest damages. It has been found that the optimal time for data acquisition is April to June for the pre-event and August to October for the post-event acquisitions. Summary For detecting bark beetle induced forest damages, the literature review shows that mono-temporal radar data are of limited use, that shorter wavelength (e.g. C-band; X-band) have a higher potential than longer wavelength such as L-band and that the current S1 time series data have a high potential for operational applications.
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Ricciardelli, E., D. Cimini, F. Di Paola, F. Romano, and M. Viggiano. "A statistical approach for rain class evaluation using Meteosat Second Generation-Spinning Enhanced Visible and InfraRed Imager observations." Hydrology and Earth System Sciences Discussions 10, no. 11 (2013): 13671–706. http://dx.doi.org/10.5194/hessd-10-13671-2013.
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Abstract. Precipitation measurements are essential for short term hydrological and long term climate studies. Operational networks of rain gauges and weather radars provide fairly accurate rain rate measurements, but they leave large areas uncovered. Because of this, satellite remote sensing is a useful tool for the detection and characterization of the raining areas in regions where this information remains missing. This study exploits the Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) observations to evaluate the rain class at high spatial and temporal resolutions. The Rain Class Evaluation from Infrared and Visible (RainCEIV) observations technique is proposed. The purpose of RainCEIV is to supply continuous monitoring of convective as well as of stratiform rainfall events. It applies a supervised classifier to the spectral and textural features of infrared and visible MSG-SEVIRI images to classify the cloudy pixels as non rainy, light to moderate rain, or heavy to very heavy rain. The technique considers in input also the water vapour channels brightness temperatures differences for the MSG-SEVIRI images acquired 15/30/45 min before the time of interest. The rainfall rates used in the training phase are obtained with the Precipitation Estimation at Microwave frequencies (PEMW), an algorithm for rain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-B observations. The results of RainCEIV have been validated against radar-derived rainfall measurements from the Italian Operational Weather Radar Network for some case studies limited to the Mediterranean area. The dichotomous assessment shows that RainCEIV is able to detect rainy areas with an accuracy of about 91%, a Heidke skill score of 56%, a Bias score of 1.16, and a Probability of Detection of rainy areas of 66%.
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Zhang Zhenzhong, 张振中. "基于更新分类器的合成孔径雷达图像目标识别". Laser & Optoelectronics Progress 58, № 14 (2021): 1410013. http://dx.doi.org/10.3788/lop202158.1410013.
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Fu Xiangwei, 付相为, 单慧琳 Shan Huilin, 吕宗奎 Zongkui Lü та 王兴涛 Wang Xingtao. "基于深度学习的合成孔径雷达图像去噪算法". Acta Optica Sinica 43, № 6 (2023): 0610002. http://dx.doi.org/10.3788/aos221437.
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Leblon, Brigitte. "Mapping forest clearcuts using radar digital imagery: A review of the Canadian experience." Forestry Chronicle 75, no. 4 (1999): 675–84. http://dx.doi.org/10.5558/tfc75675-4.
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Annual clearcut mapping is currently done in Canada mainly from photo-interpretation of aerial photographs. An advantageous alternative would use digital imagery. Optical imagery acquisition depends on weather and illumination conditions, but not radar images. This paper documents the state of practice in Canada in the use of radar digital images for clearcut mapping, with regards to the type of images used, to the influence of environmental conditions, the band, polarization, time of the year, and incidence angles, as well as to the mapping accuracy. Synergism between optical and radar images is also discussed. Finally, a few experimental automated mapping systems using radar imageries are presented. Key words: remote sensing, forest inventory updating, clearcut mapping, synthetic aperture radar, microwave, digital imagery
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Kulie, Mark S., Claire Pettersen, Aronne J. Merrelli, et al. "Snowfall in the Northern Great Lakes: Lessons Learned from a Multisensor Observatory." Bulletin of the American Meteorological Society 102, no. 7 (2021): E1317—E1339. http://dx.doi.org/10.1175/bams-d-19-0128.1.
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AbstractA multisensor snowfall observational suite has been deployed at the Marquette, Michigan, National Weather Service Weather Forecast Office (KMQT) since 2014. Micro Rain Radar (MRR; profiling radar), Precipitation Imaging Package (PIP; snow particle imager), and ancillary ground-based meteorological observations illustrate the unique capabilities of these combined instruments to document radar and concomitant microphysical properties associated with northern Great Lakes snowfall regimes. Lake-effect, lake-orographic, and transition event case studies are presented that illustrate the variety of snowfall events that occur at KMQT. Case studies and multiyear analyses reveal the ubiquity of snowfall produced by shallow events. These shallow snowfall features and their distinctive microphysical fingerprints are often difficult to discern with conventional remote sensing instruments, thus highlighting the scientific and potential operational value of MRR and PIP observations. The importance of near-surface lake-orographic snowfall enhancement processes in extreme snowfall events and regime-dependent snow particle microphysical variability controlled by regime and environmental factors are also highlighted.
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Zhirov, A. I., A. K. Monakhov, and M. A. Shubina. "Representation of Populated Places on Radar Imagery." Mapping Sciences and Remote Sensing 40, no. 3 (2003): 208–11. http://dx.doi.org/10.2747/0749-3878.40.3.208.
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Harvey, E. R., and G. V. April. "Speckle reduction in synthetic-aperture-radar imagery." Optics Letters 15, no. 13 (1990): 740. http://dx.doi.org/10.1364/ol.15.000740.
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Quinquis, A., E. Radoi, and F. C. Totir. "Some Radar Imagery Results Using Superresolution Techniques." IEEE Transactions on Antennas and Propagation 52, no. 5 (2004): 1230–44. http://dx.doi.org/10.1109/tap.2004.827541.
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Belotsercovsky, Andrey V., Hiroshi Uyeda, and Katsuhiro Kikuchi. "Radar imagery nowcasting using adaptive stochastic models." Atmospheric Research 34, no. 1-4 (1994): 249–57. http://dx.doi.org/10.1016/0169-8095(94)90095-7.
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